Organizations: the National Center of Technology Innovation for Intelligent Design and Numerical Control, Huazhong University of Science and Technology, Wuhan 430074, China · the Department of Intelligent Manufacturing, Contemporary Amperex Technology Ltd., Ningde 352000, China · the Institute for Infocomm Research, A*STAR, Singapore 138632 · the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 · the School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082, China
Industrial 3D anomaly detection performance is fundamentally constrained by the scarcity and long-tailed distribution of abnormal samples. To address this challenge, we propose Synthesis4AD, an end-to-end paradigm that leverages large-scale, high-fidelity synthetic anomalies to learn more discriminative representations for 3D anomaly detection. At the core of Synthesis4AD is 3D-DefectStudio, a software platform built upon the controllable synthesis engine MPAS, which injects geometrically realistic defects guided by higher-dimensional support primitives while simultaneously generating accurate point-wise anomaly masks. Furthermore, Synthesis4AD incorporates a multimodal large language model (MLLM) to interpret product design information and automatically translate it into executable anomaly synthesis instructions, enabling scalable and knowledge-driven anomalous data generation. To improve the robustness and generalization of the downstream detector on unstructured point clouds, Synthesis4AD further introduces a training pipeline based on spatial-distribution normalization and geometry-faithful data augmentations, which alleviates the sensitivity of Point Transformer architectures to absolute coordinates and improves feature learning under realistic data variations. Extensive experiments demonstrate state-of-the-art performance on Real3D-AD, MulSen-AD, and a real-world industrial parts dataset. The proposed synthesis method MPAS and the interactive system 3D-DefectStudio will be publicly released at https://github.com/hustCYQ/Synthesis4AD.
Figures & tables
TABLE I: Comparison of supported anomaly categories between current 3D anomaly synthesis methods and our MPAS method. MPAS enables diverse and realistic defect generation across multiple dimensions of primitives.
Fig. 1: Comparison between previous methods and our method in 3D anomaly synthesis. In (b), from top to bottom, the rows illustrate anomalies synthesized based on 1D, 2D, and 3D primitives, respectively.
Fig. 2: MPAS framework leverages different dimensional primitives to automatically synthesize massive, realistic, and diverse 3D anomalous data.
Fig. 3: Overview of the proposed Synthesis4AD system. Stage I parses product-side knowledge, including expert priors, multi-view cues, and textual specifications, into executable synthesis instructions via an MLLM, and drives 3D-DefectStudio to inject controllable anomalies into large-scale normal 3D assets. Stage II trains the anomaly detector using the generated anomalous samples and their ground-truth masks. Stage III deploys the trained model for prototype-based online inference, producing both point-wise anomaly maps and object-wise anomaly scores from scanned test data.
Fig. 4: Visualization of anomalies. From top to bottom: real anomalies, Synthesized anomalies by MPAS with the same types, and two rows of more diverse compound anomalies synthesized by MPAS. Red insets highlight defect regions for detailed comparison.
Fig. 5: t-SNE visualization of feature distributions. Normal samples and real anomalies are compared with synthetic anomalies generated by MPAS, GLFM, and R3D-AD.
Method →
CPMF
R3D-AD
R3D-AD
Reg3D-AD
Group3AD
IMRNet
ISMP
PO3AD
MC3D-AD
GLFM
Synthesis4AD
Category ↓
PR’24
ECCV’24
+MPAS
NeurIPS’23
ACM MM’24
CVPR’24
AAAI’25
CVPR’25
IJCAI’25
TASE’25
Ours
Airplane
63.2/61.8
76.5/61.4
81.3/61.4
71.6/63.1
74.4/63.6
76.2/-
85.8 / 75.3
80.4/-
85.0 /62.8
54.6/74.3
62.1/ 83.6
Car
51.8/83.6
68.2/62.0
74.4/62.0
69.7/71.8
72.8/74.5
71.1/-
73.1/83.6
65.4/-
74.9/81.9
84.2 / 88.2
96.3 / 95.7
Candybar
71.8/73.4
66.4/61.2
72.0/61.2
82.7/72.4
84.7 /73.8
75.5/-
85.2 / 90.7
78.5/-
83.0/ 91.0
71.5/79.7
79.8/87.6
Chicken
64.0/55.9
69.7/53.9
69.7/53.9
85.2 /67.6
78.6 /75.9
78.0/-
71.4/ 79.8
68.6/-
71.5/64.0
68.8/62.4
74.2/ 78.5
Diamond
64.0/75.3
61.3/50.3
79.2/51.7
90.0/83.5
93.2/86.2
90.5/-
94.8 /92.6
80.1/-
95.5 / 94.2
71.2/76.8
76.3/ 95.3
TABLE II: Quantitative Results on Real3D-AD. The results are presented in O-ROC%/P-ROC%. The best performance is in bold , and the second best is underlined .
Method →
PatchCore-FP
PatchCore-FP-R
PatchCore-PM
Reg3D-AD
R3D-AD
R3D-AD
IMRNet
GLFM
Synthesis4AD
Category ↓
CVPR’22
CVPR’22
CVPR’22
NeurIPS’23
ECCV’24
+MPAS
CVPR’24
TASE’25
Ours
Capsule
89.8/91.7
90.5/91.9
90.3/ 92.1
91.2 /87.7
78.1/59.8
78.1/59.8
60.1/42.3
96.7 / 93.0
91.2 /86.0
Cotton
25.3/55.4
26.3/54.6
19.7/52.8
43.0/52.1
79.0/38.3
87.5 /45.9
58.5/50.7
81.2/ 67.9
99.8 / 75.8
Cube
72.3/57.5
66.8/43.7
72.2/41.7
56.9/62.6
73.4/60.5
73.4/60.5
43.2/56.6
75.6 / 68.0
90.2 / 80.5
Spring pad
98.6/62.9
100 /60.1
96.5/62.1
95.1/ 80.2
44.4/49.5
82.6/59.7
65.1/40.1
100 /70.5
100 / 77.7
Screw
97.9/57.8
93.1/61.0
99.7 /59.7
97.2/54.0
56.2/49.7
80.2/ 61.3
74.2/45.6
63.6/60.9
99.3 / 66.3
TABLE III: Quantitative Results on MulSen-AD. The results are presented in O-ROC%/P-ROC%. The best performance is in bold , and the second best is underlined .
Fig. 6: Qualitative comparison of on representative categories. From top to bottom: input point clouds ( P ), ground-truth masks (GT), and predicted anomaly maps produced by R3D-AD, GLFM, and our Synthesis4AD. Warmer colors indicate higher anomaly scores.
Fig. 7: Feature distribution visualization of MC3D-AD, GLFM, and Synthesis4AD. All methods utilize the identical PointMAE backbone.
MPAS-1D
MPAS-2D
MPAS-3D
Real3D-AD
MulSen-AD
✓
✗
✗
75.6/79.4
81.3/68.3
✗
✓
✗
73.3/76.2
80.8/65.1
✗
✗
✓
77.6/81.8
84.6/69.9
✓
✓
✗
77.2/80.5
83.7/69.4
✓
✗
✓
78.4/83.2
86.2/70.8
✓
✓
✓
80.9/84.8
89.6/72.0
TABLE IV: Ablation results on the impact of different anomaly types used during training.
Fig. 8: Effect of synthetic anomaly scale on detection performance. O-ROC and P-ROC on Real3D-AD and MulSen-AD are reported as the number of synthesized anomalous training samples increases. The dashed lines indicate the previous SOTA results.
SDN
Rotations
Perturbations
Dropout
Real3D-AD
MulSen-AD
✗
✗
✗
✗
74.7/77.5
82.4/65.2
✓
✗
✗
✗
78.3/82.0
86.5/68.8
✓
✓
✗
✗
79.0/83.1
87.4/70.7
✓
✓
✓
✗
80.1/84.2
88.9/71.4
✓
✓
✗
✓
79.6/83.3
87.7/71.2
✓
✓
✓
✓
80.9/84.8
89.6/72.0
TABLE V: Ablation results on the impact of SDN and different data augmentation.
Fig. 9: Real-world industrial data collection and representative defect examples. (a) The 3D scanning setup is utilized to capture point clouds of industrial parts. (b) Abnormal samples from six object categories; red boxes highlight defective position.
Method →
R3D-AD
MC3D-AD
GLFM
Synthesis4AD
Category ↓
ECCV’24
IJCAI’25
TASE’25
Ours
Bevel Gear
63.7/42.5
80.7/62.1
83.7/64.1
100 / 72.1
Brake Caliper
64.0/47.7
83.0/65.6
83.0/66.6
93.7 / 70.1
Connecting Rod
56.8/54.2
80.0/63.7
68.0/66.3
97.3 / 72.9
Crankshaft
44.3/53.1
91.3/69.5
81.3/65.8
87.7 / 69.1
Piston
65.3/60.0
79.7/61.2
82.3/66.7
96.7 / 75.9
TABLE VI: Quantitative Results on Actual Industry Parts Dataset. The results are presented in O-ROC%/P-ROC%. The best performance is in bold .
Fig. 10: Visualization of prediction results in the actual industry parts dataset using the proposed method and other methods. The first column is the original point clouds, while the second column is the ground truth. Subsequent columns depict various methods.
Point cloud anomaly detection identifies and localizes unusual 3D geometry or surface defects, often in industrial inspection with few labeled failures.
University of British Columbia, Vancouver, V6T 1Z4 BC, Canada · Vancouver Prostate Centre, M.H. Mohseni Institute of Urologic Sciences, Vancouver, V6H 3ZB BC, Canada · Department of Urologic Sciences, The University of British Columbia, Vancouver, V5Z 1M9 BC, Canada
School of Computer Science, Wuhan University, Wuhan 430072, Hubei, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518060, Guangdong, China · School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China +1
College of Computer Science and Software Engineering and the Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China